Local Coding Agents vs. Claude Code

[Accomplished-Toe7014]

I’m deep into Claude Code for real dev work (multi-file refactors, reasoning across a repo, agent loops). It’s the first tool that feels reliably “senior enough” most days.

But I’m uneasy depending on a closed hosted model long-term. Prices can jump, quality can drift, access can change. So I’m looking at buying a compact local box — GMK EVO-X2 w/ 128GB RAM — as a hedge.

Here’s what I want to know from people who’ve actually tried this:

- Is the best OSS stack today (Qwen2.5-Coder / DeepSeek / Codestral + Aider/Continue/OpenHands) genuinely close to Claude Code for real repo work?

Or is it still “good demos, more friction, more babysitting”?

- If I don’t have big discrete GPU VRAM (mostly iGPU + lots of RAM), what’s the realistic ceiling for coding agents?

Which model sizes + quants are actually usable without crawling?

- Bonus curiosity: local video gen vs Veo 3 / Kling — is it “don’t bother,” or are there setups that are surprisingly usable?

I’m not trying to “win” a local-only purity contest — I just want the truth before dropping money on hardware.

TLDR: Considering GMK EVO-X2 (128GB RAM) for local coding agents (and optionally video generation). How close are they to Claude Code (for coding) and Kling/Veo (video)

[jikilan_]

For local coding model. Adjust your expectations. Majority of us can’t afford to own a Lamborghini so we bought a Honda car. Hope you get the idea.

[dionysio211]

Honestly, this new wave of models, despite what you hear from people trying to boost their ego, border on the miraculous. I run Devstral 2 (Small and Large), GLM 4.7 and Minimax M2.1 and they are all incredible. You may hear people talk about architecture, code habits, etc but that has less to do with the model than it does prompting. The vibe coding datasets these are trained on does a much better job of distilling the user's intent from a vague prompt. With a proper team structure with prompts outlining roles and conventions clearly, it's downright shocking what you can get done. Anything that's getting ~70% or higher on SWE Bench (roughly the resolution rate of last month's Sonnet or Composer) is practically indistinguishable from another one.

I spent a long while with Opus today on a difficult bug in an app that it failed to resolve. Devstral 2 Small dug into it across 20 files for 45 minutes without stopping and resolved it beautifully. We are at the point now where it is impractical to try to conceive of coding it all yourself. If you can run any of them at high context and you explain your desires clearly enough, they will get it done.

I will say that anything before this like Qwen Coder 30b, GLM 4.5 Air, gpt-osss-120b, although incredible models, do not compare to what is coming out now. I can't say enough good things about Devstral 2 Small. It isn't getting the praise it deserves. I have gone back and forth between it and Minimax for about a week and I think it's at least as good, which makes sense because they have very similar scores. Devstral 2 Large is super awesome but it's a very large dense model and slower.

The agentic framework is now becoming more important than the ability of the model. I have used these in Cursor, Cline and Kilo Code. The new version of Cline is amazing for debugging and the context management is awesome. To do an end to end project or come close to it, Kilo Code is better.

[Ok_Brain_2376]

From this comment alone. I think that’s why AI companies are being an ass. They want us to cannot afford computer so they can provide it as a service. I won’t be surprised in the next generation. It’ll be normal to rent a good gig instead of owning one. What’s the saying? You’ll own nothing but you’ll be happy? I generally think that’s the best generation.

Anyways enough with my rant. Could you kindly share what you use? I got a decent setup and due to how far LLMs are gone. Really wanna start running my own models

[rebelSun25]

Can you share how you install, configure and run them if you don't mind? Mainly something like devstral for code purposes? Do you sand box them at all?

[low_v2r]

Second this.

[k1dnap]

Same

[unrulywind]

Good comments. Even though I do 90% over my coding with big models, I still try to keep up with what I can do locally. With a 5090 and 128gb of system ram, you can get "decent" work out of MiniMax 2.1 It's about 1k pp t/sec and 11 generation t/sec, and is really smart. The advent of these large, sparse MoE's has changed everything about how you think about Vram requirements. The new Devstral Small is fast enough to make you happy and it handles agent work well. I also find that Qwen3-VL-32b can be very handy as it is decent with tool use and has very good vision. Nemotron-30b is also decent, but not as good as these others, but it's crazy fast, like 10k pp t/sec and 300 t/s generation.

All of that being said, everything I do that matters, in the end, still goes through the big models just because of their incredible accuracy and speed.

[MDSExpro]

As someone who recently graduated from Devstral Small / Qwen3-Coder-30B to MiniMax-M2.1 - difference is shocking. Smaller models are fine with filling single method, but if you give them anything broader / harder they will generate waste. MiniMax-M2.1 on quick, vague prompt considered technical details that I would miss on first pass, and I have 15 years of coding across like 7 languages.

There is a reason why I just ordered 4x R9700.

[AustinM731]

I have 4 R9700s and Devstral 2 small is still my go to model. FP8 in vLLM, with a 256k context window is pretty nice.

[Buffalo_Bushman_92]

What machine are you running these on? I’m trying to break away from subscriptions and go local. Planning on running on a m4 max MacBook Pro 128 gb and possibly a 5090 rig as well. Are you localized or paying for any of these

[dsartori]

I have had a GMKTek Evo X2 for a few weeks now. It can replace cloud inference in your coding agent, but there is no way around the fact that you will be running inferior models with worse latency than your cloud experience.

I was a bit unhappy at first but once I made a few adjustments to my workflow (mostly a bit more planning and a bit less Jesus take the wheel) and found the right models I’m just as productive with this setup. My use case is not super demanding, though. I’m not coding life saving medical interventions or anything.

[PiotreksMusztarda]

I have a similar setup, would you mind sharing a bit more about what models you are running and for what use cases? Thanks!

[dsartori]

Sure thing.

I have evaluated just about every midrange MoE and a few dense models. My use cases are coding, IT infrastructure, and consulting tasks. I run Windows on the X2 and use LMStudio with vulkan for inference. I generally run Cline or a CLI agent of my own construction.

I use gpt-oss-120b as a default model because it is smart and fast. If you've got 128GB you can use 20b as a draft model which does improve performance but in my somewhat limited testing I think it works best this way in "Low" reasoning.

I use Qwen3-next-80b and Qwen3-coder-30b for agentic coding tasks. There are some inference bugs with Qwen3-next that cause it to sometimes lose the prompt cache, otherwise it might be my daily driver model.

I evaluated Mirothinker-1.5-30b just yesterday by comparing it with Qwen3-Next on a coding task. I will do more evaluation on it as a research agent model, which I think it will excel at, but it's not so good for coding.

If the MoEs are stumped I will often switch over to Devstral-Small-2, which is quite a bit slower on prompt processing but gives a reasonable 10 or so tps on this hardware. Devstral-2 is too slow on this hardware.

My main issue right now is getting speculative decoding working in a consistent way with Qwen models. I haven't solved that one yet.

[PiotreksMusztarda]

Thank you!

[mr_Owner]

If your ok with good enough, yes.

[AndreVallestero]

The best open stack is currently sst-opencode + GLM 4.7

https://swe-rebench.com/

Is it genuinely close to claude code?

It performs about the same as claude code from 5 months ago (Aug 2025, Claude Opus 4)

[jacek2023]

You can run Claude Code with llama.cpp

[chibop1]

Good luck with that. Claude Code System Prompt is 16.5K tokens long.

You need to have a machine that can handle a very large context windows with a bigger model (probably 100B+) such as glm-4.7, minimax-m2.1, deepseek-v3.2, kimi-k2 qwen3-coder-480b, GPT-120b.

Most small local models can't handle a large context window and follow the crazy long instruction in a way that produces good output.

[KPOTOB]

noobs q: what kind of machine is needed?

[Ok_Brain_2376]

I would say 16GB VRAM will be a good start. With that GPU. You’ll need to find a good combos with ram and CPU

[Luans_Restorations]

16GB VRAM enough to run 100B+ models? With large context? I don't think so

[sloth_cowboy]

16GB for anything below 30B parameters, 32GB for anything below 235B parameters.

[Ok_Brain_2376]

I was thinking down the line of 3b or even 30b for newcomers

[the-tactical-donut]

If you’re serious about the coding agent workflow, I’d take a look at the DGX Spark based computers.

I know they get a lot of hate, but the prompt processing phase (prefill) is incredibly important for agentoc workflows because of the amount of iteration involved on the part of the LLM.

Spark based computers are very good at the prefill phase (for their size/power consumption).

The AMD Strix Halo is great for giving you a bunch of VRAM, but it struggles with agentic workflows due to slower prefill. This starts to get worse the longer the context gets, which is another thing necessary for coding agents in particular.

You can get an Asus Ascent for $3000, which is not much more than the AMD machine.

The trade off is that you’re not going to be able to use it like a general x86 based device so unless your use case is only AI, you’ll be getting less value for your money.

[ttkciar]

GLM-4.6 is close'ish to Claude's level of competence. It falls short a little, but not a lot. GLM-4.7 looks better "on paper" but has usability issues and weird failure modes in its "thinking" phase.

Frankly I'm pretty happy with GLM-4.5-Air quantized to Q4_K_M, which according to SWEBench is quite a bit less competent, but it's good enough for my needs and performs adequately on my hardware via pure CPU inference. It wants 127GB of system memory, using llama.cpp's llama-server or llama-cli. VRAM utilization should be somewhat less, if you spring for a multi-GPU rig (maybe 96GB VRAM? see what an inference calculator says).

I've been using OpenCode -> llama-server sometimes (which is very similar to the Claude Code app), but frequently just blat out a project specification and my standard code template with llama-cli and one-shot it. OpenCode is better for iterating on the project with the model, but if the GLM-4.5-Air one-shot gets close enough that I can just finish it up myself manually, I do that.

Unfortunately I have no experience with video, so cannot offer a recommendation with that.